This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of SS 2020
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
|available module versions|
|SS 2021||WS 2020/1||SS 2020||SS 2019||SS 2012||WS 2011/2|
MA3402 is a semester module in English language at Master’s level which is offered in summer semester.
This module description is valid to WS 2021/2.
|Total workload||Contact hours||Credits (ECTS)|
|150 h||45 h||5 CP|
Content, Learning Outcome and Preconditions
- know how discrete and continuous random variables/vectors are generated using statistical software such as R
- understand Bayesian principles, such as prior, posterior distributions
- understand the theory of MCMC algorithms from selected examples
- are able to construct MCMC algorithms to simulate from the posterior distributions and to assess convergence of MCMC simulations
- know how to use Bootstrap and Jacknife methods to estimate standard errors of estimators
- know how to apply the EM algorithm to missing data problems
- are able to program statistical algorithms in the statistical software package R
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VO||2||Computational Statistics [MA3402]||Ankerst, D. Miller, G.||
Tue, 10:15–11:45, virtuell
|UE||1||Computational Statistics (Exercise Session) [MA3402]||Ankerst, D. Miller, G.||dates in groups|
Learning and Teaching Methods
Description of exams and course work
The exam may be repeated at the end of the semester.